Numerous health-related variables show spatial distributions. Fields such as human genetics, population genetics, demography, epidemiology, and public health furnish such data and this project is concerned with appropriate statistical techniques for analyzing them and inferring underlying spatial processes from observed spatial patterns. We propose to develop a test of significance for the difference between pairs or sheaves of spatial correlograms and to look at new ways to describe spatial dependence. The properties of plots of phenetic versus geographic distances will be examined. The results of spatial autocorrelation studies will be compared with those of several other spatial statistical techniques. To substantiate our analytical procedures we plan computer simulation studies in which selection and migration interact with isolation-by-distance to create gene frequency surfaces. A different simulation study will address problems of migration, selection and genetic drift at the interpopulation level. The special problems of physiographic barriers will be examined in connection with both simulations. Spatial autocorrelation analysis will be applied to patterns of blood group and other gene frequency distributions in Europe and elsewhere. We shall isolate and identify components due to selection, migration, isolation-by-distance and genetic drift by combined consideration of geographic variation patterns and spatial correlograms. The data will be studied on geographic scales ranging from continents to clusters of villages. The analyses will be combined with numerical cladistic approaches in order to relate current distribution patterns with prehistoric and historical evidence on population migrations. An overall analysis of Europe based on 2099 locality samples of 16 human polymorphic systems may be completed on this project depending on the outcome of other grant applications. Detailed analysis will be made for five countries in Europe and for several finer scale data sets such as village and island groups. A special analysis of the population structure of the Yanomama Indian will be carried out in collaboration with Dr. J. M. Neel of the University of Michigan.